class_docstr: Defines a vocabulary object that will be used to numericalize a field.
  
  Attributes:
      freqs: A collections.Counter object holding the frequencies of tokens
          in the data used to build the Vocab.
      stoi: A collections.defaultdict instance mapping token strings to
          numerical identifiers.
      itos: A list of token strings indexed by their numerical identifiers.
class_docstr_tok: ["Defines","a","vocabulary","object","that","will","be","used","to","numericalize","a","field",".","Attributes",":","freqs",":","A","collections.Counter","object","holding","the","frequencies","of","tokens","in","the","data","used","to","build","the","Vocab",".","stoi",":","A","collections.defaultdict","instance","mapping","token","strings","to","numerical","identifiers",".","itos",":","A","list","of","token","strings","indexed","by","their","numerical","identifiers","."]
code: def _default_unk_index(self):
      return self.unk_index
  
file_path: pytext/pytext/legacy/vocab.py
imports: import logging
  from collections import Counter, defaultdict
  import torch
  from torchtext.vocab import pretrained_aliases, Vectors
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling the Vocab obj's underlying member method code with example usage: obj._default_unk_index() and returns: self